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Schematic representation of the mean atmospheric boundary layer flow around an isolated sharp-edged low-rise building (modified from Hosker 1984).
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This article provides an overview of the application of computational fluid dynamics (CFD) in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) wind-driven rain on building facades, (3) convective heat transfer coefficients at exterior building surfaces and (4)...
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... with the two most commonly used semi-empirical WDR models identified some important physical deficiencies in these models (Blocken et al. 2010b). Also, a sensitivity study demonstrated the very large impact of changes in heat transfer coefficients and the related mass transfer coefficients on the drying behaviour of ceramic bricks in facades (Janssen et al. 2007a). The information provided by empirical and semi-empirical formulae is often also too simplified compared to the well-established building performance simulation tools in which this information is used, such as Building Envelope Heat-Air-Mass (BE-HAM) transfer tools and Building Energy Simulation (BES) software. Numerical modelling with CFD can be a powerful alternative because it can avoid some of these limitations. It can provide detailed information on the relevant flow variables in the whole calculation domain (“whole-flow field data”), under well-controlled conditions and without similarity constraints. However, the accuracy of CFD is an important matter of concern. Care is required in the geometrical implementation of the model, in grid generation and in selecting proper solution strategies and parameters. The latter include choices between steady Reynolds-averaged Navier-Stokes (RANS), unsteady RANS (URANS), Large Eddy Simulation (LES) or hybrid URANS/LES, between different turbulence models or subgrid-scale turbulence models, discretisation schemes, etc. In addition, numerical and physical modelling errors need to be assessed by solution verification and validation studies. This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) WDR on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. These four topics were chosen for four reasons: (1) they represent cases of varying physical complexity (single-phase flow, multi-phase flow with particles, heat transfer and multi-component gas flow); (2) they are in practice most often addressed by traditional approaches; i.e. either wind tunnel experiments or (semi-)empirical formulae; (3) CFD has some specific advantages for these topics compared to the traditional approaches; and (4) CFD is currently at a state in which it can technically be applied for these topics. First, in section 2, the wind-flow pattern around an isolated building is briefly described and the early CFD simulations of wind flow around an isolated building are discussed, as they provided the basis for the later applications. Section 3 lists a number of best practice guideline documents for CFD that were developed in the past decade. The overview with focus on the four topics is presented in sections 4-7. The overview is not intended to cover all previous research efforts in each of these topics, but rather to highlight specific difficulties, advantages and disadvantages of CFD. The wind-flow pattern around an isolated building is briefly discussed to support the explanations in the following sections. Figure 2 provides a schematic illustration of the wind-flow pattern. As the wind approaches the building, it gradually diverges. At the windward facade (not shown in figure), a stagnation point with maximum pressure is situated at approximately 60-70% of the building height. From this point, the flow is deviated to the lower pressure zones of the facade: upwards, sidewards and downwards. The upward and sideward flow separate at the upwind facade edges, and create a separation bubble or recirculation zone characterised by low velocity and high turbulence intensity. Depending on the building dimensions and the turbulence of the oncoming flow, the separated flow can reattach to the side facades and roof (as illustrated in Figure 2 by the dotted reattachment lines). A considerable amount of air flows downwards from the stagnation point and produces a vortex at ground level (called the standing vortex, frontal vortex or horseshoe vortex). The main flow direction of the standing vortex near ground level is opposite to the direction of the approach flow. Where both flows meet, a stagnation point with low wind speed values exists at ground level, upstream of the building (not shown in figure). The standing vortex stretches out sideways and sweeps around the building corners creating corner streams with high wind speeds. At the leeward side of the building, an underpressure zone exists. As a result, backflow or recirculation flow occurs in a cavity zone that consists of vortices with horizontal and vertical axes (i.e. the near wake). The mean cavity reattachment line downstream of the building marks the end of the cavity zone. Beyond this location, the flow resumes its normal direction but wind speed stays low for a considerable distance behind the building (i.e. the far wake). It is important to note that Figure 2 only shows the mean wind-flow pattern, and that the actual flow pattern exhibits pronounced transient features, such as the build-up and collapse of the separation/recirculation bubbles and periodic vortex shedding in the wake. Figure 2 also only shows the mean wind-flow pattern for a single building. In multi-building configurations, the flow patterns can interact, yielding a higher complexity. CFD simulation of wind flow around buildings started with fundamental studies for isolated buildings, often with a cubical shape, to analyse the velocity and pressure fields (Vasilic-Melling 1977, Hanson al. 1986, Paterson and Apelt, 1986, 1989, 1990, Murakami et al. 1987, 1990, 1992, Murakami and Mochida, 1988, 1989, Baskaran and Stathopoulos, 1989, 1992, Stathopoulos and Baskaran 1990, Murakami 1990a, 1990b, 1993, Baetke et al. 1990, Mochida et al. 1993). Together with later studies, they laid the foundations for the current best practice guidelines, by focusing on the importance of grid resolution (Murakami and Mochida 1989, Murakami 1990a, 1990b, Baskaran and Stathopoulos 1992), the influence of the boundary conditions on the numerical results (Murakami and Mochida 1989, Paterson and Apelt 1990, Baetke et al. 1990, Stathopoulos and Baskaran 1990, Baskaran and Stathopoulos 1992) and by comparing the performance of various types of turbulence models in steady RANS simulations (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Mochida et al. 2002). Also comparisons of RANS versus LES were performed (Murakami et al. 1990, 1992, Murakami 1990b, 1993). Note that in steady RANS simulations, only the mean flow is solved, while all scales of turbulence are modelled (i.e., approximated). In LES on the other hand, the large and generally most important turbulent eddies are explicitly resolved, while only the eddies smaller than a user-defined filter are modelled. In the past, especially the deficiencies of using the steady RANS approach with the standard k- ε model (Jones and Launder 1972) for wind flow around buildings were addressed. These include the stagnation point anomaly with overestimation of turbulent kinetic energy near the frontal corner and the resulting underestimation of the size of separation and recirculation regions on the roof and the side faces, and the underestimation of turbulent kinetic energy in the wake resulting in an overestimation of the size of the cavity zone and wake. Various revised linear and non- linear k- ε models and also second-moment closure models were developed and tested, and showed improved performance for several parts of the flow field (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Wright et al. 2001, Mochida et al. 2002). However, the main limitation of steady RANS modelling remained: its incapability to model inherently transient features of the flow field such as separation and recirculation downstream of windward edges and vortex shedding in the wake. These features can be explicitly resolved by LES. While URANS has hardly been used to study wind flow around buildings, early applications of LES for this purpose were already made by Murakami et al. in 1987, and later (Murakami et al. 1990, 1992, Murakami 1990b). These studies illustrated the superior performance of LES compared to RANS. The studies mentioned above are not all studies that were performed for isolated buildings. But starting from the 1990s, supported by the previous studies and the increased computing performance and availability of CFD codes, fundamental studies gradually shifted their focus to multiple-building configurations, and also application studies were increasingly performed. The sensitivity of the CFD results to the wide range of computational parameters to be set by the user and the possibility of applying CFD in practice led to the development of best practice guidelines in the past decades, as discussed in the next section. In CFD simulations, a large number of choices needs to be made by the user. It is well-known that these choices can have a very large impact on the results. In a typical CFD simulation, the user has to choose the approximate equations describing the flow (steady RANS, URANS, LES or hybrid URANS/LES), the level of detail in the geometrical representation of the buildings, the size of the computational domain, the boundary conditions, the computational grid, the discretisation schemes, the initialisation data, the time step size and the iterative convergence criteria. Already since the start of the application of CFD for outdoor environment studies in the late 70-ies and 80-ies, researchers have been testing the influence of these parameters on the results, which has provided a lot of valuable information (e.g. Murakami and Mochida 1989, Baetke et al. 1990, Stathopoulos and Baskaran 1990, Cowan et al. 1997, Hall 1997). However, this information was dispersed over a large number of individual publications in different journals, ...
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... coefficients and the related mass transfer coefficients on the drying behaviour of ceramic bricks in facades (Janssen et al. 2007a). The information provided by empirical and semi-empirical formulae is often also too simplified compared to the well-established building performance simulation tools in which this information is used, such as Building Envelope Heat-Air-Mass (BE-HAM) transfer tools and Building Energy Simulation (BES) software. Numerical modelling with CFD can be a powerful alternative because it can avoid some of these limitations. It can provide detailed information on the relevant flow variables in the whole calculation domain (“whole-flow field data”), under well-controlled conditions and without similarity constraints. However, the accuracy of CFD is an important matter of concern. Care is required in the geometrical implementation of the model, in grid generation and in selecting proper solution strategies and parameters. The latter include choices between steady Reynolds-averaged Navier-Stokes (RANS), unsteady RANS (URANS), Large Eddy Simulation (LES) or hybrid URANS/LES, between different turbulence models or subgrid-scale turbulence models, discretisation schemes, etc. In addition, numerical and physical modelling errors need to be assessed by solution verification and validation studies. This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) WDR on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. These four topics were chosen for four reasons: (1) they represent cases of varying physical complexity (single-phase flow, multi-phase flow with particles, heat transfer and multi-component gas flow); (2) they are in practice most often addressed by traditional approaches; i.e. either wind tunnel experiments or (semi-)empirical formulae; (3) CFD has some specific advantages for these topics compared to the traditional approaches; and (4) CFD is currently at a state in which it can technically be applied for these topics. First, in section 2, the wind-flow pattern around an isolated building is briefly described and the early CFD simulations of wind flow around an isolated building are discussed, as they provided the basis for the later applications. Section 3 lists a number of best practice guideline documents for CFD that were developed in the past decade. The overview with focus on the four topics is presented in sections 4-7. The overview is not intended to cover all previous research efforts in each of these topics, but rather to highlight specific difficulties, advantages and disadvantages of CFD. The wind-flow pattern around an isolated building is briefly discussed to support the explanations in the following sections. Figure 2 provides a schematic illustration of the wind-flow pattern. As the wind approaches the building, it gradually diverges. At the windward facade (not shown in figure), a stagnation point with maximum pressure is situated at approximately 60-70% of the building height. From this point, the flow is deviated to the lower pressure zones of the facade: upwards, sidewards and downwards. The upward and sideward flow separate at the upwind facade edges, and create a separation bubble or recirculation zone characterised by low velocity and high turbulence intensity. Depending on the building dimensions and the turbulence of the oncoming flow, the separated flow can reattach to the side facades and roof (as illustrated in Figure 2 by the dotted reattachment lines). A considerable amount of air flows downwards from the stagnation point and produces a vortex at ground level (called the standing vortex, frontal vortex or horseshoe vortex). The main flow direction of the standing vortex near ground level is opposite to the direction of the approach flow. Where both flows meet, a stagnation point with low wind speed values exists at ground level, upstream of the building (not shown in figure). The standing vortex stretches out sideways and sweeps around the building corners creating corner streams with high wind speeds. At the leeward side of the building, an underpressure zone exists. As a result, backflow or recirculation flow occurs in a cavity zone that consists of vortices with horizontal and vertical axes (i.e. the near wake). The mean cavity reattachment line downstream of the building marks the end of the cavity zone. Beyond this location, the flow resumes its normal direction but wind speed stays low for a considerable distance behind the building (i.e. the far wake). It is important to note that Figure 2 only shows the mean wind-flow pattern, and that the actual flow pattern exhibits pronounced transient features, such as the build-up and collapse of the separation/recirculation bubbles and periodic vortex shedding in the wake. Figure 2 also only shows the mean wind-flow pattern for a single building. In multi-building configurations, the flow patterns can interact, yielding a higher complexity. CFD simulation of wind flow around buildings started with fundamental studies for isolated buildings, often with a cubical shape, to analyse the velocity and pressure fields (Vasilic-Melling 1977, Hanson al. 1986, Paterson and Apelt, 1986, 1989, 1990, Murakami et al. 1987, 1990, 1992, Murakami and Mochida, 1988, 1989, Baskaran and Stathopoulos, 1989, 1992, Stathopoulos and Baskaran 1990, Murakami 1990a, 1990b, 1993, Baetke et al. 1990, Mochida et al. 1993). Together with later studies, they laid the foundations for the current best practice guidelines, by focusing on the importance of grid resolution (Murakami and Mochida 1989, Murakami 1990a, 1990b, Baskaran and Stathopoulos 1992), the influence of the boundary conditions on the numerical results (Murakami and Mochida 1989, Paterson and Apelt 1990, Baetke et al. 1990, Stathopoulos and Baskaran 1990, Baskaran and Stathopoulos 1992) and by comparing the performance of various types of turbulence models in steady RANS simulations (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Mochida et al. 2002). Also comparisons of RANS versus LES were performed (Murakami et al. 1990, 1992, Murakami 1990b, 1993). Note that in steady RANS simulations, only the mean flow is solved, while all scales of turbulence are modelled (i.e., approximated). In LES on the other hand, the large and generally most important turbulent eddies are explicitly resolved, while only the eddies smaller than a user-defined filter are modelled. In the past, especially the deficiencies of using the steady RANS approach with the standard k- ε model (Jones and Launder 1972) for wind flow around buildings were addressed. These include the stagnation point anomaly with overestimation of turbulent kinetic energy near the frontal corner and the resulting underestimation of the size of separation and recirculation regions on the roof and the side faces, and the underestimation of turbulent kinetic energy in the wake resulting in an overestimation of the size of the cavity zone and wake. Various revised linear and non- linear k- ε models and also second-moment closure models were developed and tested, and showed improved performance for several parts of the flow field (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Wright et al. 2001, Mochida et al. 2002). However, the main limitation of steady RANS modelling remained: its incapability to model inherently transient features of the flow field such as separation and recirculation downstream of windward edges and vortex shedding in the wake. These features can be explicitly resolved by LES. While URANS has hardly been used to study wind flow around buildings, early applications of LES for this purpose were already made by Murakami et al. in 1987, and later (Murakami et al. 1990, 1992, Murakami 1990b). These studies illustrated the superior performance of LES compared to RANS. The studies mentioned above are not all studies that were performed for isolated buildings. But starting from the 1990s, supported by the previous studies and the increased computing performance and availability of CFD codes, fundamental studies gradually shifted their focus to multiple-building configurations, and also application studies were increasingly performed. The sensitivity of the CFD results to the wide range of computational parameters to be set by the user and the possibility of applying CFD in practice led to the development of best practice guidelines in the past decades, as discussed in the next section. In CFD simulations, a large number of choices needs to be made by the user. It is well-known that these choices can have a very large impact on the results. In a typical CFD simulation, the user has to choose the approximate equations describing the flow (steady RANS, URANS, LES or hybrid URANS/LES), the level of detail in the geometrical representation of the buildings, the size of the computational domain, the boundary conditions, the computational grid, the discretisation schemes, the initialisation data, the time step size and the iterative convergence criteria. Already since the start of the application of CFD for outdoor environment studies in the late 70-ies and 80-ies, researchers have been testing the influence of these parameters on the results, which has provided a lot of valuable information (e.g. Murakami and Mochida 1989, Baetke et al. 1990, Stathopoulos and Baskaran 1990, Cowan et al. 1997, Hall 1997). However, this information was dispersed over a large number of individual publications in different journals, conference proceedings 1 and reports. In 2000, the ERCOFTAC Special Interest Group on Quality and Trust in Industrial CFD published an extensive set of best practice guidelines for industrial CFD users (Casey and Wintergerste 2000). The ...
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... well established for indoor environment applications, this is considerably less pronounced for outdoor environment applications. In complex case studies, wind environmental problems such as pedestrian wind nuisance and air pollutant dispersion are still typically investigated in atmospheric boundary layer wind tunnels (Stathopoulos 2002), while WDR exposure and convective heat transfer coefficients at exterior building surfaces are generally estimated from simplified empirical or semi-empirical formulae (Blocken and Carmeliet 2004a, 2010a, Palyvos 2008, Defraeye et al. 2010). An important disadvantage of wind tunnel measurements however is that usually only point measurements are obtained. Techniques such as Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) in principle allow planar or even full 3D data to be obtained, but the cost is considerably higher and application for complicated geometries can be hampered by laser-light shielding by the obstructions constituting the urban model. Another disadvantage is the required adherence to similarity criteria in reduced-scale testing. This can be a problem for, e.g., multi-phase flow problems and buoyant flows. Examples are WDR and pollutant dispersion studies. Empirical and semi-empirical formulae generally only provide a first, crude indication of the relevant parameters, often in averaged form (e.g., surface- averaged) or at a few discrete positions. Examples are the semi-empirical formulae for WDR intensities (Lacy 1965, Sanders 1996, Straube and Burnett 2000, Blocken and Carmeliet 2004a, 2010a, 2010b, ISO 2009) and the (semi-)empirical expressions for convective heat transfer coefficients (e.g. Sharples 1984, Loveday and Taki 1996, Liu and Harris 2007, Palyvos 2008). In addition, a recent study comparing validated CFD simulations with the two most commonly used semi-empirical WDR models identified some important physical deficiencies in these models (Blocken et al. 2010b). Also, a sensitivity study demonstrated the very large impact of changes in heat transfer coefficients and the related mass transfer coefficients on the drying behaviour of ceramic bricks in facades (Janssen et al. 2007a). The information provided by empirical and semi-empirical formulae is often also too simplified compared to the well-established building performance simulation tools in which this information is used, such as Building Envelope Heat-Air-Mass (BE-HAM) transfer tools and Building Energy Simulation (BES) software. Numerical modelling with CFD can be a powerful alternative because it can avoid some of these limitations. It can provide detailed information on the relevant flow variables in the whole calculation domain (“whole-flow field data”), under well-controlled conditions and without similarity constraints. However, the accuracy of CFD is an important matter of concern. Care is required in the geometrical implementation of the model, in grid generation and in selecting proper solution strategies and parameters. The latter include choices between steady Reynolds-averaged Navier-Stokes (RANS), unsteady RANS (URANS), Large Eddy Simulation (LES) or hybrid URANS/LES, between different turbulence models or subgrid-scale turbulence models, discretisation schemes, etc. In addition, numerical and physical modelling errors need to be assessed by solution verification and validation studies. This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) WDR on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. These four topics were chosen for four reasons: (1) they represent cases of varying physical complexity (single-phase flow, multi-phase flow with particles, heat transfer and multi-component gas flow); (2) they are in practice most often addressed by traditional approaches; i.e. either wind tunnel experiments or (semi-)empirical formulae; (3) CFD has some specific advantages for these topics compared to the traditional approaches; and (4) CFD is currently at a state in which it can technically be applied for these topics. First, in section 2, the wind-flow pattern around an isolated building is briefly described and the early CFD simulations of wind flow around an isolated building are discussed, as they provided the basis for the later applications. Section 3 lists a number of best practice guideline documents for CFD that were developed in the past decade. The overview with focus on the four topics is presented in sections 4-7. The overview is not intended to cover all previous research efforts in each of these topics, but rather to highlight specific difficulties, advantages and disadvantages of CFD. The wind-flow pattern around an isolated building is briefly discussed to support the explanations in the following sections. Figure 2 provides a schematic illustration of the wind-flow pattern. As the wind approaches the building, it gradually diverges. At the windward facade (not shown in figure), a stagnation point with maximum pressure is situated at approximately 60-70% of the building height. From this point, the flow is deviated to the lower pressure zones of the facade: upwards, sidewards and downwards. The upward and sideward flow separate at the upwind facade edges, and create a separation bubble or recirculation zone characterised by low velocity and high turbulence intensity. Depending on the building dimensions and the turbulence of the oncoming flow, the separated flow can reattach to the side facades and roof (as illustrated in Figure 2 by the dotted reattachment lines). A considerable amount of air flows downwards from the stagnation point and produces a vortex at ground level (called the standing vortex, frontal vortex or horseshoe vortex). The main flow direction of the standing vortex near ground level is opposite to the direction of the approach flow. Where both flows meet, a stagnation point with low wind speed values exists at ground level, upstream of the building (not shown in figure). The standing vortex stretches out sideways and sweeps around the building corners creating corner streams with high wind speeds. At the leeward side of the building, an underpressure zone exists. As a result, backflow or recirculation flow occurs in a cavity zone that consists of vortices with horizontal and vertical axes (i.e. the near wake). The mean cavity reattachment line downstream of the building marks the end of the cavity zone. Beyond this location, the flow resumes its normal direction but wind speed stays low for a considerable distance behind the building (i.e. the far wake). It is important to note that Figure 2 only shows the mean wind-flow pattern, and that the actual flow pattern exhibits pronounced transient features, such as the build-up and collapse of the separation/recirculation bubbles and periodic vortex shedding in the wake. Figure 2 also only shows the mean wind-flow pattern for a single building. In multi-building configurations, the flow patterns can interact, yielding a higher complexity. CFD simulation of wind flow around buildings started with fundamental studies for isolated buildings, often with a cubical shape, to analyse the velocity and pressure fields (Vasilic-Melling 1977, Hanson al. 1986, Paterson and Apelt, 1986, 1989, 1990, Murakami et al. 1987, 1990, 1992, Murakami and Mochida, 1988, 1989, Baskaran and Stathopoulos, 1989, 1992, Stathopoulos and Baskaran 1990, Murakami 1990a, 1990b, 1993, Baetke et al. 1990, Mochida et al. 1993). Together with later studies, they laid the foundations for the current best practice guidelines, by focusing on the importance of grid resolution (Murakami and Mochida 1989, Murakami 1990a, 1990b, Baskaran and Stathopoulos 1992), the influence of the boundary conditions on the numerical results (Murakami and Mochida 1989, Paterson and Apelt 1990, Baetke et al. 1990, Stathopoulos and Baskaran 1990, Baskaran and Stathopoulos 1992) and by comparing the performance of various types of turbulence models in steady RANS simulations (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Mochida et al. 2002). Also comparisons of RANS versus LES were performed (Murakami et al. 1990, 1992, Murakami 1990b, 1993). Note that in steady RANS simulations, only the mean flow is solved, while all scales of turbulence are modelled (i.e., approximated). In LES on the other hand, the large and generally most important turbulent eddies are explicitly resolved, while only the eddies smaller than a user-defined filter are modelled. In the past, especially the deficiencies of using the steady RANS approach with the standard k- ε model (Jones and Launder 1972) for wind flow around buildings were addressed. These include the stagnation point anomaly with overestimation of turbulent kinetic energy near the frontal corner and the resulting underestimation of the size of separation and recirculation regions on the roof and the side faces, and the underestimation of turbulent kinetic energy in the wake resulting in an overestimation of the size of the cavity zone and wake. Various revised linear and non- linear k- ε models and also second-moment closure models were developed and tested, and showed improved performance for several parts of the flow field (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Wright et al. 2001, Mochida et al. 2002). However, the main limitation of steady RANS modelling remained: its incapability to model inherently transient features of the flow field such as separation and recirculation downstream of windward edges and vortex shedding in the wake. These features can be explicitly resolved by LES. While URANS has hardly been used to study wind flow around buildings, early applications of LES for this purpose were already made ...
Context 4
... Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) in principle allow planar or even full 3D data to be obtained, but the cost is considerably higher and application for complicated geometries can be hampered by laser-light shielding by the obstructions constituting the urban model. Another disadvantage is the required adherence to similarity criteria in reduced-scale testing. This can be a problem for, e.g., multi-phase flow problems and buoyant flows. Examples are WDR and pollutant dispersion studies. Empirical and semi-empirical formulae generally only provide a first, crude indication of the relevant parameters, often in averaged form (e.g., surface- averaged) or at a few discrete positions. Examples are the semi-empirical formulae for WDR intensities (Lacy 1965, Sanders 1996, Straube and Burnett 2000, Blocken and Carmeliet 2004a, 2010a, 2010b, ISO 2009) and the (semi-)empirical expressions for convective heat transfer coefficients (e.g. Sharples 1984, Loveday and Taki 1996, Liu and Harris 2007, Palyvos 2008). In addition, a recent study comparing validated CFD simulations with the two most commonly used semi-empirical WDR models identified some important physical deficiencies in these models (Blocken et al. 2010b). Also, a sensitivity study demonstrated the very large impact of changes in heat transfer coefficients and the related mass transfer coefficients on the drying behaviour of ceramic bricks in facades (Janssen et al. 2007a). The information provided by empirical and semi-empirical formulae is often also too simplified compared to the well-established building performance simulation tools in which this information is used, such as Building Envelope Heat-Air-Mass (BE-HAM) transfer tools and Building Energy Simulation (BES) software. Numerical modelling with CFD can be a powerful alternative because it can avoid some of these limitations. It can provide detailed information on the relevant flow variables in the whole calculation domain (“whole-flow field data”), under well-controlled conditions and without similarity constraints. However, the accuracy of CFD is an important matter of concern. Care is required in the geometrical implementation of the model, in grid generation and in selecting proper solution strategies and parameters. The latter include choices between steady Reynolds-averaged Navier-Stokes (RANS), unsteady RANS (URANS), Large Eddy Simulation (LES) or hybrid URANS/LES, between different turbulence models or subgrid-scale turbulence models, discretisation schemes, etc. In addition, numerical and physical modelling errors need to be assessed by solution verification and validation studies. This paper provides an overview of the application of CFD in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) WDR on building facades, (3) convective heat transfer coefficients at exterior building surfaces, and (4) air pollutant dispersion around buildings. These four topics were chosen for four reasons: (1) they represent cases of varying physical complexity (single-phase flow, multi-phase flow with particles, heat transfer and multi-component gas flow); (2) they are in practice most often addressed by traditional approaches; i.e. either wind tunnel experiments or (semi-)empirical formulae; (3) CFD has some specific advantages for these topics compared to the traditional approaches; and (4) CFD is currently at a state in which it can technically be applied for these topics. First, in section 2, the wind-flow pattern around an isolated building is briefly described and the early CFD simulations of wind flow around an isolated building are discussed, as they provided the basis for the later applications. Section 3 lists a number of best practice guideline documents for CFD that were developed in the past decade. The overview with focus on the four topics is presented in sections 4-7. The overview is not intended to cover all previous research efforts in each of these topics, but rather to highlight specific difficulties, advantages and disadvantages of CFD. The wind-flow pattern around an isolated building is briefly discussed to support the explanations in the following sections. Figure 2 provides a schematic illustration of the wind-flow pattern. As the wind approaches the building, it gradually diverges. At the windward facade (not shown in figure), a stagnation point with maximum pressure is situated at approximately 60-70% of the building height. From this point, the flow is deviated to the lower pressure zones of the facade: upwards, sidewards and downwards. The upward and sideward flow separate at the upwind facade edges, and create a separation bubble or recirculation zone characterised by low velocity and high turbulence intensity. Depending on the building dimensions and the turbulence of the oncoming flow, the separated flow can reattach to the side facades and roof (as illustrated in Figure 2 by the dotted reattachment lines). A considerable amount of air flows downwards from the stagnation point and produces a vortex at ground level (called the standing vortex, frontal vortex or horseshoe vortex). The main flow direction of the standing vortex near ground level is opposite to the direction of the approach flow. Where both flows meet, a stagnation point with low wind speed values exists at ground level, upstream of the building (not shown in figure). The standing vortex stretches out sideways and sweeps around the building corners creating corner streams with high wind speeds. At the leeward side of the building, an underpressure zone exists. As a result, backflow or recirculation flow occurs in a cavity zone that consists of vortices with horizontal and vertical axes (i.e. the near wake). The mean cavity reattachment line downstream of the building marks the end of the cavity zone. Beyond this location, the flow resumes its normal direction but wind speed stays low for a considerable distance behind the building (i.e. the far wake). It is important to note that Figure 2 only shows the mean wind-flow pattern, and that the actual flow pattern exhibits pronounced transient features, such as the build-up and collapse of the separation/recirculation bubbles and periodic vortex shedding in the wake. Figure 2 also only shows the mean wind-flow pattern for a single building. In multi-building configurations, the flow patterns can interact, yielding a higher complexity. CFD simulation of wind flow around buildings started with fundamental studies for isolated buildings, often with a cubical shape, to analyse the velocity and pressure fields (Vasilic-Melling 1977, Hanson al. 1986, Paterson and Apelt, 1986, 1989, 1990, Murakami et al. 1987, 1990, 1992, Murakami and Mochida, 1988, 1989, Baskaran and Stathopoulos, 1989, 1992, Stathopoulos and Baskaran 1990, Murakami 1990a, 1990b, 1993, Baetke et al. 1990, Mochida et al. 1993). Together with later studies, they laid the foundations for the current best practice guidelines, by focusing on the importance of grid resolution (Murakami and Mochida 1989, Murakami 1990a, 1990b, Baskaran and Stathopoulos 1992), the influence of the boundary conditions on the numerical results (Murakami and Mochida 1989, Paterson and Apelt 1990, Baetke et al. 1990, Stathopoulos and Baskaran 1990, Baskaran and Stathopoulos 1992) and by comparing the performance of various types of turbulence models in steady RANS simulations (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Mochida et al. 2002). Also comparisons of RANS versus LES were performed (Murakami et al. 1990, 1992, Murakami 1990b, 1993). Note that in steady RANS simulations, only the mean flow is solved, while all scales of turbulence are modelled (i.e., approximated). In LES on the other hand, the large and generally most important turbulent eddies are explicitly resolved, while only the eddies smaller than a user-defined filter are modelled. In the past, especially the deficiencies of using the steady RANS approach with the standard k- ε model (Jones and Launder 1972) for wind flow around buildings were addressed. These include the stagnation point anomaly with overestimation of turbulent kinetic energy near the frontal corner and the resulting underestimation of the size of separation and recirculation regions on the roof and the side faces, and the underestimation of turbulent kinetic energy in the wake resulting in an overestimation of the size of the cavity zone and wake. Various revised linear and non- linear k- ε models and also second-moment closure models were developed and tested, and showed improved performance for several parts of the flow field (Baskaran and Stathopoulos 1989, Murakami et al. 1992, Murakami 1993, Wright et al. 2001, Mochida et al. 2002). However, the main limitation of steady RANS modelling remained: its incapability to model inherently transient features of the flow field such as separation and recirculation downstream of windward edges and vortex shedding in the wake. These features can be explicitly resolved by LES. While URANS has hardly been used to study wind flow around buildings, early applications of LES for this purpose were already made by Murakami et al. in 1987, and later (Murakami et al. 1990, 1992, Murakami 1990b). These studies illustrated the superior performance of LES compared to RANS. The studies mentioned above are not all studies that were performed for isolated buildings. But starting from the 1990s, supported by the previous studies and the increased computing performance and availability of CFD codes, fundamental studies gradually shifted their focus to multiple-building configurations, and also application studies were increasingly performed. The sensitivity of the CFD results to the wide range of computational parameters to be set by the user and the possibility of applying CFD in practice led to the development of best practice ...
Citations
... 17 In particular, the unique characteristics of building wakes shown in Fig. 2, comprise the recirculation zone, roof-top and side vortices on the top and windward sides, the horseshoe vortex on the leeward ground, and the stagnation point on the windward building front. 18,19 The wind flow patterns around multiple buildings are more complex and difficult to model as compared to a single structure. The complexity increases with buildings of different heights, asymmetric wind incidence angles and buildings surface roughness. ...
The low altitude economy presents new opportunities for urban mobility with drones and air taxis offering solutions to traffic congestion and access to hard-to-reach areas. These opportunities come with the same challenges as in civil aviation: the atmospheric boundary layer (ABL) gives rise to wind gusts and turbulence, leading to sudden displacements of the low-altitude vehicles from the planned trajectory. Hence, a reliable estimation and prediction of wind conditions is paramount to safe drone and air taxi operations. Drones offer an attractive alternative to expensive stationary platforms for wind monitoring in the lower ABL. This article reviews the status, challenges, opportunities, and alternatives to drone-based wind anemometry. Aspects include the wind measurement and estimation of urban terrain, performance metrics of anemometers, model-based wind speed measurement, and machine learning algorithms for atmospheric wind prediction. Already the attitude of the hovering and cruising drones allows to infer the wind velocity. The accuracy of drone wind measurement may be further augmented with various anemometers such as ultrasonic, thermal, hot wire, and pitot tubes. This article exemplifies the potential of drone anemometry for urban wind prediction, for measuring wind profiles in remote areas of wind turbines as well as a tool for boundary layer meteorology and assessment of gusts in harsh weather conditions.
... Computational Fluid Dynamics (CFD) (Anderson and Wendt, 1995) has transformed the way engineers study fluid behavior across disciplines from aerospace engineering (Slotnick et al., 2014), civil engineering (Blocken et al., 2011), to biomedical engineering (Doost et al., 2016). However, CFD simulation remains highly specialized and requires a wide range of expertise in fluid mechanics, numerical methods, geometric reasoning, and high-performance computing. ...
Computational Fluid Dynamics (CFD) is an essential simulation tool in various engineering disciplines, but it often requires substantial domain expertise and manual configuration, creating barriers to entry. We present Foam-Agent, a multi-agent framework that automates complex OpenFOAM-based CFD simulation workflows from natural language inputs. Our innovation includes (1) a hierarchical multi-index retrieval system with specialized indices for different simulation aspects, (2) a dependency-aware file generation system that provides consistency management across configuration files, and (3) an iterative error correction mechanism that diagnoses and resolves simulation failures without human intervention. Through comprehensive evaluation on the dataset of 110 simulation tasks, Foam-Agent achieves an 83.6% success rate with Claude 3.5 Sonnet, significantly outperforming existing frameworks (55.5% for MetaOpenFOAM and 37.3% for OpenFOAM-GPT). Ablation studies demonstrate the critical contribution of each system component, with the specialized error correction mechanism providing a 36.4% performance improvement. Foam-Agent substantially lowers the CFD expertise threshold while maintaining modeling accuracy, demonstrating the potential of specialized multi-agent systems to democratize access to complex scientific simulation tools. The code is public at https://github.com/csml-rpi/Foam-Agent
... While direct measurements are the most reliable way to characterize IAQ accurately, they are the most expensive, time-consuming, potentially hazardous, and difficult to apply for extensive parametric analyses. Therefore, researchers have advocated using numerical simulations in air quality assessment [47][48][49][50][51]. For instance, Ma and Zhou [52] investigated a sour gas well blowout scenario through CFD analysis. ...
... While providing detailed insights into airflow patterns and contaminant dispersion, CFD and other sophisticated modeling approaches impose substantial computational burdens; these methods typically require significant processing power and time to generate accurate results, making them impractical for continuous, real-time monitoring applications [66]. The computational intensity of these approaches becomes particularly problematic when scaling to large buildings or multiple facilities, as the resources required grow exponentially with the size and complexity of the monitored space [51]. Moreover, the need for specialized expertise to interpret and utilize these complex models adds another layer of operational overhead. ...
People spend a significant portion of their time in enclosed spaces, making indoor air quality (IAQ) a critical factor for health and productivity. Artificial intelligence (AI)-driven systems that monitor air quality in real-time and utilize historical data for accurate forecasting have emerged as effective solutions to this challenge. However, these systems often raise privacy concerns, as they may inadvertently expose sensitive information about occupants’ habits and presence. Addressing these privacy challenges is essential. This research comprehensively reviews the existing literature on traditional and AI-based IAQ management, focusing on privacy-preserving techniques. The analysis reveals that while significant progress has been made in IAQ monitoring, most systems prioritize accuracy at the expense of privacy. Existing approaches often fail to adequately address the risks associated with data collection and the implications for occupant privacy. Emerging AI-driven technologies, such as federated learning and edge computing, offer promising solutions by processing data locally and minimizing privacy risks. This research introduces a novel AI-based IAQ management platform incorporating the SITA (Spatial, Identity, Temporal, and Activity) model. By leveraging customizable privacy settings, the platform enables users to safeguard sensitive information while ensuring effective IAQ management. Integrating Internet of Things (IoT) sensor networks, edge computing, and advanced privacy-preserving technologies, the proposed system delivers a robust and scalable solution that protects both privacy and health.
... Over the past ten years, numerous studies have focused on computational fluid dynamics (CFD) modeling within the realm of urban physics, particularly addressing airflow and pollutant dispersion. Common benchmark scenarios typically involve airflow around individual buildings or clusters of structures with uniform or similar geometric shapes, simulating a simplified urban environment [14][15][16][17]. Research has extensively explored how the arrangement of objects influences flow patterns [18,19], pollutant dispersion [20,21], and urban ventilation [22,23] (Castro et al., 2017;Tong et al., 2016) within idealized city layouts characterized by regularly or irregularly positioned obstacles. ...
Urban traffic-related air pollution has emerged as a significant concern for the physical environment in densely populated urban areas. This study numerically investigates the dispersion of air pollutants and ventilation within typical urban blocks in Shanghai, considering the prevailing annual winds—northerly in winter (4.64 m/s) and easterly in summer (5.85 m/s). Multiple factors influence the dispersion of urban pollution. In this research, we examine the effects of viaducts and urban ventilation corridors, alongside the impact of urban parameters on pedestrian-level ventilation, by analyzing variations in building forms along residential streets in Shanghai. A novel approach for analyzing pollution dispersion is proposed, which involves performing a sensitivity analysis on the buffer radius and mapping various radii onto the C* parameter. The results indicate that: (1) enhancing air fluidity in regions with stagnant winds can be achieved by introducing vertical turbulence; (2) the prevailing wind direction, urban ventilation corridors, and urban permeability play a crucial role in determining the direction of pollutant dispersion at pedestrian levels in densely populated urban environments; (3) the contribution of pollutants released at ground level is significantly higher than those from viaducts at pedestrian height (248.58%). Drawing on both theoretical and experimental research, this study explores the spatial dispersion of air pollutants across various scales, including city-wide, block-level, and building-specific perspectives. The findings provide recommendations for the design of environmentally sustainable urban streets in residential areas.
... Over the years, numerous studies have characterized WDR exposure of building facades across various countries, primarily using semi-empirical approaches based on the so-called "WDR relationship" [7,[12][13][14][15][16]. Occasionally, the analysis of simultaneous DRWP complemented these characterizations, providing general benchmark indices for comparing exposures among different locations and establishing qualitative design requirements to mitigate rainwater intrusion on building facades [7,[17][18][19]. At the building scale, WDR and DRWP exposures vary significantly based on facade height and geometry, as well as the surrounding terrain, thereby altering the watertightness requirements for facades within the same location [20,21]. ...
Rainwater penetration into building facades results in multiple issues, including material and structural degradation, reduced energy efficiency, and health-related concerns among occupants. Currently, the watertightness performance of building facades is assessed based on standardized tests, which simulate generic water supplies and pressure differentials that do not reflect the specific exposure conditions of each facade. Consequently, practitioners’ decisions regarding facade design often rely on qualitative and imprecise criteria that do not align with the actual climatic loads. In this article, a comprehensive approach to facade design for preventing rainwater penetration is described, incorporating specific methodological refinements for reliable and practical implementation across various Spanish regions. In this approach, the parameters surpassed during any watertightness test (defined by the magnitude and duration of the water supplies and pressure differentials) are correlated with the recurrence of equivalent climatic exposures at the facade (determined by the climatic conditions of the site, facade height, and surrounding environment), thereby quantitatively characterizing the facade watertightness performance. The findings used to refine this method for implementation in Spain are illustrated and validated using selected case studies, and a comprehensive database is provided to enable its application at 360 locations distributed across various regions of the country.
... The findings presented here echo a commonly seen feature in RANS modeling given that across most of the hours both the standard k-ε model and the SST k-ω model underpredict the wind speed observed at the McTavish weather station. In wake regions such as the one at this station, RANS models are known to underestimate the TKE, which results in lower wind speeds [69]. The issue is exacerbated when the actual wind speed in the region is low, which was the case near the weather station across the hours simulated. ...
... Verification and validation studies are very important for increased confidence in CFD simulation results. Thus, it is a viable alternative to wind tunnel tests [78,79]. One of the main advantages of CFD simulations that makes them suitable for urban wind assessment applications is their ability to produce detailed visualisations and point measurements of different flow variables around and inside the studied urban configurations [58,71]. ...
... Since there should be a fine balance between the accuracy and the time needed for running the simulations, the investigation recommended the realizable k-ε RANS model for reaching this balance for urban wind flow problems. In that study, a variety of turbulence models were tested and the realizable k-ε yielded consistent results without taking a significant time to solve the flow [78]. ...
Due to the complex nature of the built environment, urban wind flow is unpredictable and characterised by high levels of turbulence and low mean wind speed. Yet, there is a potential for harnessing urban wind power by carefully integrating wind turbines within the built environment at the optimum locations. This requires a thorough investigation of wind resources to use the suitable wind turbine technology at the correct location—thus, the need for an accurate assessment of wind resources at the proposed site. This paper reviews the commonly used wind assessment tools for the urban wind flow to identify the optimum tool to be used prior to integrating wind turbines in urban areas. In situ measurements, wind tunnel tests, and CFD simulations are analysed and reviewed through their advantages and disadvantages in assessing urban wind flows. The literature shows that CFD simulations are favoured over other most commonly used tools because the tool is relatively easier to use, more efficient in comparing alternative design solutions, and can effectively communicate data visually. The paper concludes with recommendations on best practice guidelines for using CFD simulation in assessing the wind flow within the built environment and emphasises the importance of validating CFD simulation results by other available tools to avoid any associated uncertainties.
... Traditionally, urban microclimate analysis is performed based on observational methods (Toparlar et al., 2015). With the growing accessibility of computational resources, researchers have been utilizing numerical simulation methods (Blocken, Stathopoulos, Carmeliet, & Hensen, 2011). The main advantage of numerical simulations is the ability to generate various scenarios, referred to as comparative analyses (Blocken, 2015). ...
... Logarithmic law is assumed to describe the atmospheric boundary layer flow (ABL) at the inlet of CD; see Eq. (4) (Blocken, 2015;Blocken et al., 2011), where * friction velocity, the von Karman constant (0.42) and the height coordinate. The turbulent kinetic energy and the turbulence dissipation rate are given by Eqs. ...
... The environment is intrinsically associated with the occurrence and prevalence, influencing transmission pathways by affecting the dispersal of microorganisms or vectors [80,81]. Leveraging principles from fluid dynamics [82,83], ecological simulation methods are utilized to simulate the wind fields and water flows within the analysis area. By overlaying potential sources of infection, such as wet markets or landfills, it becomes possible to analyze the spatial spread range of pathogens or viruses, delineating multi-level impact areas. ...
The occurrence and spread of infectious diseases pose considerable challenges to public health. While the relationship between the built environment and the spread of infectious diseases is well-documented, there is a dearth of urban planning tools specifically designed for conducting Health Impact Assessments (HIAs) targeted at infectious diseases. To bridge this gap, this paper develops a comprehensive framework of an HIA for Urban Planning and Epidemic (HIA4UPE), formulated by considering the progression of public health incidents and the distinct transmission patterns of infectious diseases. This framework is designed to provide a comprehensive assessment by including a health risk-overlay assessment, health resource-quality assessment, health resource-equality assessment, and health outcome-impact prediction, enabling a multidimensional evaluation of the potential impacts of current environmental conditions or planning proposals on the incidence of infectious diseases. Furthermore, this paper advances the application of spatial analysis and computation, comprehensive assessment methodologies, and predictive analytics to conduct specific assessments. The theoretical framework and analytical tools presented in this paper contribute to the academic discourse and offer practical utility in urban planning and policymaking on epidemic prevention and control.
... It was ensured that no cells around important areas were larger than 1 m. The pedestrian level height (1.75 m) was divided by at least 0.5 m sized cells in height, as has been previously recommended [24,93]. On the other hand, geometric elements with details more intricate than 1 m and which may impact the wind flow were also divided using high resolution meshes. ...
(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.
